An Evolutionary Artificial Neural Network for Medical Pattern Classifi

被引：0

作者：

Tan, Shing Chiang ^{[1
]}

Lim, Chee Peng ^{[2
]}

Tan, Kay Sin ^{[3
]}

Navarro, Jose C. ^{[4
]}

机构：

[1] Multimedia Univ, Fac Informat Sci & Technol, Melaka Campus, Bukit Beruang 75450, Melaka, Malaysia

[2] Univ Sci Malaysia, Sch Elect & Elect Engn, George Town 14300, Malaysia

[3] Univ Malaya, Fac Med, Dept Med, Kuala Lumpur 50603, Malaysia

[4] Unive Santo Tomas Hosp, Manila, Philippines

来源：

NEURAL INFORMATION PROCESSING, PT 2, PROCEEDINGS | 2009年 / 5864卷

关键词：

Fuzzy ARTMAP; Hybrid Genetic Algorithms; Pattern Classification; Medical Decision Support; ARCHITECTURE; DIAGNOSIS; SYSTEM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a novel evolutionary artificial neural network based on the integration between Fuzzy ARTMAP (FAM) and a Hybrid Genetic Algorithm (HGA) is proposed for tackling medical pattern classification tasks. To assess the effectiveness of the proposed FAM-HGA model, the Ripley artificial data set is first used, and the results are compared with those of FAM-GA and FAM. A real medical data set comprising anonymous stroke patient records is then employed for further experimentation. The performance of FAM-HGA is assessed using three indicators; accuracy, sensitivity and specificity, and the results are compared with those of FAM-GA and FAM. Overall, FAM-HGA yields better classification performances than FAM-GA and FAM. The study reveals the potential of FAM-HGA as a computerized decision support tool for medical pattern classification tasks.

引用

页码：475 / +

页数：2

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